Whole Bone Strain Quantification by Image Registration: A Validation Study
Bibliographic record
Abstract
Quantification of bone strain can be used to better understand fracture risk, bone healing, and bone turnover. The objective of this work was to develop and validate an intensity matching image registration method to accurately measure and spatially resolve strain in vertebrae using microCT imaging. A strain quantification method was developed that used two sequential microCT scans, taken in loaded and unloaded configurations. The image correlation algorithm implemented was a multiresolution intensity matching deformable registration that found a series of affine mapping between the unloaded and loaded scans. Once the registration was completed, the displacement field and strain field were calculated from the mappings obtained. Validation was done in two distinct ways: the first was to look at how well the method could quantify zero strain; the second was to look at how the method was able to reproduce a known applied strain field. Analytically defined strain fields that linearly varied in space and strain fields resulting from finite element analysis were used to test the strain measurement algorithm. The deformable registration method showed very good agreement with all cases imposed, establishing a detection limit of 0.0004 strain and displaying agreement with the imposed strain cases (average R2=0.96). The deformable registration routine developed was able to accurately measure both strain and displacement fields in whole rat vertebrae. A rigorous validation of any strain measurement method is needed that reports on the ability of the routine to measure strain in a variety of strain fields with differing spatial extents, within the structure of interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".